Good perception is indispensable to perform problem solving
well. This is a common issue in cognitive psychology, but has been seldom
studied in artificial intelligence. We formalize a perception scheme for
an intelligent software using vector spaces. We also construct an experimental
system for text documents and still images. Using the system for the texts,
we conduct experiments in a context of information retrieval. The experiments
using FAQ (Frequently Asked Questions) documents prove the method to be
outperforming the conventional method. As for the system for the images,
we extract common features out of a certain set of images in terms of Kansei
engineering. The features explain characteristics of the images in intuitive
manner. We aim to construct a decision support system, which cna analyze
multimedia data from various viewpoints; however we will need to extend
the method in various ways to achieve that.